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Sparse Coding for Alpha Matting.
- Source :
-
IEEE Transactions on Image Processing . Jul2016, Vol. 25 Issue 7, p3032-3043. 12p. - Publication Year :
- 2016
-
Abstract
- Existing color sampling-based alpha matting methods use the compositing equation to estimate alpha at a pixel from the pairs of foreground ( F ) and background ( B ) samples. The quality of the matte depends on the selected ( $F,B$ ) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to ( $F,B$ ) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms the current stateoftheart in image and video matting [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 25
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 115293898
- Full Text :
- https://doi.org/10.1109/TIP.2016.2555705